[R-meta] rma.mv: why some var components change but others don't across 2 models

[Stefanou Revesz]

Sure, to confirm differences between the two models, can we say model
`res` (i.e., list(~ 1 | study, ~1|outcome, ~ 1 | measure)) views the
random effects this way:

res_model <- with(m, interaction(study,outcome,measure))

But model `res2` (i.e., list(~ 1 | study/outcome, ~ 1 | measure))
views random effects this way:

res2_model <- with(m, interaction(interaction(study,outcome), measure))

Is this correct?

Stefanou

On Sat, Oct 30, 2021 at 11:23 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
> These are totally different models, so I would not read anything into this. It is purely a coincidence.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >Sent: Saturday, 30 October, 2021 18:19
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: R meta
> >Subject: Re: rma.mv: why some var components change but others don't across 2
> >models
> >
> >Wolfgang, you're a lifesaver! That's such a confusing coincidence!
> >
> >As we inch toward the last few studies, the variance component for
> >'outcome' across `res` (fully crossed model), and `res2` (nested +
> >crossed model) get more and more similar.
> >
> >Does this say anything about the data structure up to these last few
> >studies vs. that of the last few studies? (I'm still in shock, and
> >want to rationalize why this is happening to me)
> >
> >res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1 | outcome, ~ 1 |
> >measure), data=m, subset=study <= 54)
> >res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 |
> >measure), data=m, subset=study <= 54)
> >
> >Stefanou
> >
> >On Sat, Oct 30, 2021 at 11:03 AM Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >>
> >> The values are not exactly identical and it is coincidence that they end up
> >looking that way when rounded to 4 decimal places. For example try:
> >>
> >> res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1 | outcome, ~ 1 | measure),
> >data=m, subset=study <= 20)
> >> res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 | measure),
> >data=m, subset=study <= 20)
> >>
> >> and they are rather different.
> >>
> >> Best,
> >> Wolfgang
> >>
> >> >-----Original Message-----
> >> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >> >Sent: Saturday, 30 October, 2021 15:06
> >> >To: Viechtbauer, Wolfgang (SP)
> >> >Cc: R meta
> >> >Subject: Re: rma.mv: why some var components change but others don't across 2
> >> >models
> >> >
> >> >Dear Wolfgang,
> >> >
> >> >Thank you for your reply. I did check that previously. But my question is why
> >> >'outcome' gives the same variance component across both res (with 4 levels)
> >and
> >> >res2 (with 68 levels) models?
> >> >
> >> >Thank you so much,
> >> >Stefanou
> >> >
> >> >On Sat, Oct 30, 2021, 7:08 AM Viechtbauer, Wolfgang (SP)
> >> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >> >Dear Stefanou,
> >> >
> >> >With the way you have 'outcome' coded, these two formulations are not
> >equivalent.
> >> >I believe this post discusses this:
> >> >
> >> >https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html
> >> >
> >> >Best,
> >> >Wolfgang
> >> >
> >> >>-----Original Message-----
> >> >>From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >> >>Sent: Friday, 29 October, 2021 17:24
> >> >>To: R meta
> >> >>Cc: Viechtbauer, Wolfgang (SP)
> >> >>Subject: rma.mv: why some var components change but others don't across 2
> >models
> >> >>
> >> >>Dear Wolfgang and Expert List Members,
> >> >>
> >> >>Why `study` with 57 levels in model `res` gives `sigma^2.1 = 0.0200`
> >> >>but `study` with 57 levels in model `res2` gives `sigma^2.1  =
> >> >>0.0122`?
> >> >>(SAME LEVELS BUT DIFFERENT RESULTS)
> >> >>
> >> >>Why `outcome` with 4 levels in model `res` gives `sigma^2.2 = 0.0093`
> >> >>but `outcome` with 68 levels in model `res2` gives `sigma^2.2  =
> >> >>0.0093`?
> >> >>(DIFFERENT LEVELS BUT SAME RESULTS)
> >> >>
> >> >>For reproducibility, below are my data and code.
> >> >>
> >> >>Many thanks to you all,
> >> >>Stefanou
> >> >>
> >> >>m <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/c.csv")
> >> >>
> >> >>res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1|outcome, ~ 1 |
> >> >>measure), data=m)
> >> >>                    estim       sqrt  nlvls  fixed   factor
> >> >>sigma^2.1  0.0200  0.1415     57     no    study
> >> >>sigma^2.2  0.0093  0.0964      4     no  outcome
> >> >>sigma^2.3  0.0506  0.2249      7     no  measure
> >> >>
> >> >>res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 |
> >> >>measure), data=m)
> >> >>                    estim       sqrt  nlvls  fixed         factor
> >> >>sigma^2.1  0.0122  0.1105     57     no          study
> >> >>sigma^2.2  0.0093  0.0964     68     no  study/outcome
> >> >>sigma^2.3  0.0363  0.1904      7     no        measure